5 Best Practices for Load Balancing Kubernetes Containers

471 VIEWS

In this article, we will explore what a Kubernetes load balancer is, how it can be configured, and what the best practices are to enable a Kubernetes load balancer, in your enterprise.

Kubernetes is an open-source container-orchestration system that automates web application development, scaling, and management. While Google designed Kubernetes, it is now the Cloud Native Computing Foundation that maintains it.

With its help, you can enable the operation of an elastic web server framework for cloud applications. It also supports data center outsourcing to public cloud service providers. Since Kubernetes deals with overlay networking, web hosting at scale, ingress controllers, etc., users might be stuck with load balancing challenges.

Since Kubernetes is the container orchestration system for several enterprises, it needs features like fail safety and load balancing even more. For non-container environments, load balancing is a fairly easy task. However, containers complicate this task, which is why a Kubernetes load balancer is crucial. 

What Is Kubernetes Load Balancer?

Load balancing is the process of distributing tasks over a set of resources to make the processing faster and more efficient. It optimizes the response time and evenly distributes tasks to avoid overloading compute nodes.

Kubernetes load balancer process

The Kubernetes load balancer works by sending connections to the first server in the pool until its capacity is reached. Then, it sends the new connections to the next available server. This approach is ideal for instances where virtual machines incur a cost, like in hosted environments.

The growing ecosystem of Kubernetes enables both automation and declarative configuration. It is scalable, portable, and provides widely available support and tools. Kubernetes’ pods are sets of containers related by function, grouped together as a service.

Thus, it can create and destroy pods automatically, based on user requirements. Services have their own IP address to field requests and then dispatch them to an available pod. The Kubernetes load balancer also has a scheduler that ensures the optimal running of all pods without burdening any. 

Kubernetes Load Balancing Options

There are two types of load balancers in Kubernetes.

  • Internal Load Balancers enable routing across containers within the same Virtual Private Cloud.
  • External Load Balancers direct external HTTP requests into a cluster with an IP address. The cluster then routes internet traffic to specific nodes identified by ports.

The aim of the Kubernetes load balancer is to maximize availability by distributing network traffic among backend services evenly. It also helps to ensure scalability and prevents one compute node from being overloaded while another remains idle. Kubernetes has multiple load balancing options, each with its own pros and cons. 

The most basic Kubernetes load balancer is load distribution. Two methods do this in Kubernetes, both through the kube-proxy feature. This feature manages virtual IPs that services in Kubernetes use.

  • The initial default kube-proxy mode was userspace. It allocates the next available Kubernetes pod by using round-robin load distribution on an IP list and rotating or permuting the lost.
  • Iptables, the modern kube-proxy mode, enables rule-based IP management by random selection.
  • The Ingress load balancer is flexible and popular, mostly used by cloud-based load-balancing controllers. The controller includes an Ingress resource and daemon with built-in capabilities for load balancing.

How to Configure Load Balancer in Kubernetes

Load balancing is a crucial strategy to ensure high uptime and maximum scalability in a network. To distribute traffic efficiently in the backend, Kubernetes has multiple strategies and algorithms. Here, we will discuss the four ways in which to configure the Kubernetes load balancer.

Round Robin

In the round robin method, there is a sequence of eligible servers that receive new connections. Its algorithm does not account for speed or performance variations of the individual servers, since it is static. It ensures the requests come to the servers in order.

Since round robin cannot discriminate between slow and fast servers, it allots an equal number of connections to each. This is why it is not always an ideal solution for high-performance production traffic.

Kube-proxy L4 Round Robin Load Balancing

This is one of the basic load balancing strategies in a Kubernetes cluster. It fields all requests sent to the service and then routes them. Technically, the kube-proxy is a process. It implements virtual IPs for services by using iptables rules, which adds a degree of complexity to the process.

How to Configure a Kubernetes Load Balancer

However, it adds an additional latency with each request, which might lead to problems if the number of services keep increasing.

L7 Round Robin Load Balancing

The L7 proxy routes traffic directly to Kubernetes pods by bypassing the kube-proxy through an API gateway. It manages requests for available pods and tracks which are available with the Kubernetes Endpoints API. 

On receiving a request for a specific Kubernetes service, the load balancer round robins it among relevant pods or sorts in order to find an available one.

Consistent Hashing/Ring Hash

In this algorithm, a hash based on a specified key enables the distribution of new connections across servers. It is the best solution for a large number of servers and dynamic content as it combines load balancing and persistence.

Consistent hashing does not recalculate the entire hash table for adding or removing each server. Thus, it does not interrupt other connections. It is useful for eCommerce applications and services that require a per-client state. However, it can add some amount of latency to requests when it runs at scale.

As you can see, all these Kubernetes load balancer algorithms have their advantages and disadvantages. Depending on your load, requirement, and preference, you can determine which configuration works best for your enterprise.

Best Practices for Kubernetes Load Balancer

While implementing Kubernetes load balancer in your organization, there are a few components and configuration steps that you need to take. These steps will help set up your Kubernetes system well enough to harness the powers of your load balancer.

Always check whether service load balancer is enabled 

This seems like an obvious step but it is always crucial to check that you have enabled the service load balancer in your Kubernetes system. To properly utilize Kubernetes in your application, make sure your application is designed around containerization. It should also have a load balancer that supports container environments and service discovery.

This initial step will help you take advantage of the entire system by determining whether it is suitable for your organization needs.

Enable the readiness probe on a deployment

Readiness probes let Kubernetes know when the application is ready to serve traffic. Make sure you have enabled your readiness as it passes the traffic to the pod. To ensure it is enabled, it must be defined in any deployment.

The readiness probe signals Kubernetes when to put a pod behind the load balancer and when to put the service behind the proxy. In absence of this probe, the user will be able to reach the pod but will not get a healthy server response. Hence, the pod developer needs this alert to ensure the readiness probe is enabled.

Enable the liveness probe on a deployment

A liveness probe informs Kubernetes whether a pod is healthy enough to continue working or if it should be restarted. It performs a simple check or a complex one, based on bash commands. Basically, its job is to let Kubernetes know whether the application is working.

This helps in determining whether load balancing is working fine or if some components need support. Liveness probes can determine if there is a deadlock, if an application is running and making progress, and if it is responding well. It works to increase availability even if there are bugs.

Pod Health Checks

Enable CPU/Memory requests and set limits

You should enable CPU/memory requests so that the containers in deployment can request resources automatically. This helps in freeing up CPU and memory resources needed for the system. It also defines these resources so that the pod never runs low in terms of memory.

This practice prevents the CPU or memory from taking over all resources on a node and leading to a failure or error. 

Always apply network policy

The Kubernetes load balancer needs to apply security group policies to your virtual machines or worker nodes. It is an essential measure to ensure security. Ideally, you should limit inbound and outbound traffic to the minimum requirement.

This prevents accidental exposure of unwanted services to outbound traffic flow. Kubernetes has a network security policy functionality that can serve all resources within deployments. You should also ensure that your cluster is provisioned with a network plugin that supports network policies.

Conclusion

Load balancing is essential to ensure your infrastructure stays up and running even if there is a high volume of traffic and some of your servers stop responding. Autoscaling and load balancing work together to help complex cloud Infrastructures achieve high availability.


Savan Kharod is a growth marketer (and a tech enthusiast) helping SaaS businesses drive success using Content Marketing, SEO & Paid Advertising. He has worked with some well-known SaaS companies in CX and Project Management Space.


Discussion

Click on a tab to select how you'd like to leave your comment

Leave a Comment

Your email address will not be published. Required fields are marked *